Recently, rapid urbanization around the world has spawned several urban problems. Although a large amount of experience has been accumulated throughout the process of global urban problem governance, the knowledge has not been optimally utilized. Furthermore, there is a dearth of mechanisms with which to distill and employ past experiences in addressing emerging urban problems. Consequently, in this study, based on the CBR method, we establish a mechanism called the Solution-Extracted System of Urban Problem Governance (SESUPG), aiming to find solutions to the diverse array of existing urban problems from previous experience. The main steps for obtaining a suitable solution for a specific urban problem in a target city through the SESUPG are as follows: (1) Calculate the similarity to retrieve the most similar cities. (2) Extract the possible solution through similar cities. (3) Case–solution modification before solution adoption. To verify the effectiveness of the proposed mechanism, the air pollution problem in Wuhan, China, was tested to verify the effectiveness of the SESUPG as a case study. As a result, four policy recommendations were extracted by the SESUPG, and all of them proved to be effective in mitigating air pollution problems in Wuhan. The system proposed in this study can aid decision makers in the selection of strategies and solutions when addressing urbanization issues and guiding the process of mining effective experience for the promotion of urban governance levels.

Green building has been deemed an important endeavor to promote sustainable building development. However, knowledge from different standards, different companies, and different software in the green building domain is difficult to share and reuse since different terminologies, measurement indicators, and criteria are adopted. Therefore, there is a need to create a consistent knowledge representation model in the green building domain. This study proposes a green building ontology (GB-Onto) which is an abstract conceptualization of the knowledge in the green building domain. To build the ontology more effectively, this study adopts the ontology learning method which is based on NLP and machine learning techniques. An improved TF-IDF method is introduced to extract concepts in the green building domain. Concept inclusion and semantic networks method are integrated to extract taxonomic relations. The associate rule method is used for extracting non-taxonomic relations. Finally, all these methods are implemented by adopting software and Python programming. The GB-Onto is evaluated through consistency checking and criteria-based evaluation. The GB-Onto fills the knowledge gap by providing a formal and shared vocabulary for the green building domain which promotes knowledge reuse and sharing among different stakeholders.